Evidence map›Paper›PMID 36536464›Full record

ArticleCancer imaging : the official publication of the International Cancer Imaging Society2022

Semi-quantitative and quantitative dynamic contrast-enhanced (DCE) MRI parameters as prostate cancer imaging biomarkers for biologically targeted radiation therapy.

Hayley M Reynolds, Sirisha Tadimalla, Yu-Feng Wang, Maryam Montazerolghaem, Yu Sun, Scott Williams, Catherine Mitchell, Mary E Finnegan, Declan G Murphy, Annette Haworth

Open access · goldAbstract read
In one paragraph

Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.2field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 3 countries.

Hayley M ReynoldsAuckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand. Hayley.Reynolds@auckland.ac.nz.ORCID http://orcid.org/0000-0001-8146-1113
Sirisha TadimallaSchool of Physics, The University of Sydney, Sydney, NSW, Australia.
Yu-Feng WangSchool of Physics, The University of Sydney, Sydney, NSW, Australia.
Maryam MontazerolghaemSchool of Physics, The University of Sydney, Sydney, NSW, Australia.
Yu SunSchool of Physics, The University of Sydney, Sydney, NSW, Australia.
Scott WilliamsDivision of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
Catherine MitchellDepartment of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
Mary E FinneganDepartment of Imaging, Imperial College Healthcare NHS Trust, London, UK.
Declan G MurphyDivision of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
Annette HaworthSchool of Physics, The University of Sydney, Sydney, NSW, Australia.
The University of Sydney · AUPeter MacCallum Cancer Centre · AUImperial College Healthcare NHS Trust · GBThe University of Melbourne · AUUniversity of Auckland · NZ

Funding

Health Research Council of New Zealand Health Research Council of New ZealandNational Health and Medical Research Council 1126955Prostate Cancer Foundation of Australia YI 0513
6 · The paper itself

Abstract

backgroundBiologically targeted radiation therapy treatment planning requires voxel-wise characterisation of tumours. Dynamic contrast enhanced (DCE) DCE MRI has shown promise in defining voxel-level biological characteristics. In this study we consider the relative value of qualitative, semi-quantitative and quantitative assessment of DCE MRI compared with diffusion weighted imaging (DWI) and T2-weighted (T2w) imaging to detect prostate cancer at the voxel level.

methodsSeventy prostate cancer patients had multiparametric MRI prior to radical prostatectomy, including T2w, DWI and DCE MRI. Apparent Diffusion Coefficient (ADC) maps were computed from DWI, and semi-quantitative and quantitative parameters computed from DCE MRI. Tumour location and grade were validated with co-registered whole mount histology. Kolmogorov-Smirnov tests were applied to determine whether MRI parameters in tumour and benign voxels were significantly different. Cohen's d was computed to quantify the most promising biomarkers. The Parker and Weinmann Arterial Input Functions (AIF) were compared for their ability to best discriminate between tumour and benign tissue. Classifier models were used to determine whether DCE MRI parameters improved tumour detection versus ADC and T2w alone.

resultsAll MRI parameters had significantly different data distributions in tumour and benign voxels. For low grade tumours, semi-quantitative DCE MRI parameter time-to-peak (TTP) was the most discriminating and outperformed ADC. For high grade tumours, ADC was the most discriminating followed by DCE MRI parameters Ktrans, the initial rate of enhancement (IRE), then TTP. Quantitative parameters utilising the Parker AIF better distinguished tumour and benign voxel values than the Weinmann AIF. Classifier models including DCE parameters versus T2w and ADC alone, gave detection accuracies of 78% versus 58% for low grade tumours and 85% versus 72% for high grade tumours.

conclusionsIncorporating DCE MRI parameters with DWI and T2w gives improved accuracy for tumour detection at a voxel level. DCE MRI parameters should be used to spatially characterise tumour biology for biologically targeted radiation therapy treatment planning.

Indexed as

Multiparametric Magnetic Resonance ImagingProstatic NeoplasmsBiomarkers, TumorContrast MediaDiffusion Magnetic Resonance ImagingHumansMagnetic Resonance ImagingMaleBiomarkers, TumorContrast MediaDynamic contrast enhanced MRIImaging biomarkerProstate cancerRadiation therapy

Identifiers

PMID36536464
PMCPMC9762110
OpenAlexW4312050057

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.